I hope you didn't think my regression analysis was going to end there!

Today we'll look at Receiving Fantasy Points Per Game over Expectation and the implications it has for five wide receivers in 2016.

Let's get to it.

What is RFPPGoE?

Just like yesterday, RFPPGoE is based off of the numberFire Net Expected Points (NEP) metric. On every play, there's an expected point value an NFL team has for the drive based on yard line, down, and distance. What happens on that play can change the expected point value on said drive. What NEP does is aggregate the values gained or lost on every play into a single, net number.

What's different between today and yesterday is that -- obviously -- I wanted to use Reception NEP when trying to determine how different a wide receiver's on-field play differentiated from his fantasy production.

I looked at receivers with a minimum of 100 targets from 2011 to 2015 (180 total) and plotted their Reception NEP totals against their fantasy points per game averages (PPR scoring). I also isolated the receivers' fantasy scoring to just what he did as a receiver, so no rushing or passing (I'm looking at you Mohamed Sanu) statistics were included.

The correlation for Reception NEP to receiving fantasy points per game was actually better than that for passing. I used the regression equation pictured above (with Reception NEP as "x") to create Expected Receiving Fantasy Points Per Game (ERFPPG). Then, all I had to do was subtract a player's actual fantasy scoring by his expected scoring to arrive at Receiving Fantasy Points Per Game over Expectation.

Can RFPPGoE Be Predictive?

Just like yesterday, the benefit of RFPPGoE is that it allows you to see the difference between on-field play and fantasy scoring. In theory, receivers with a lower expected than actual receiving fantasy points per game did better than their play on the field would suggest, while those with a higher expected than actual receiving fantasy points per game left some fantasy scoring on the field.

But can this tell us anything about what will happen the following year?

Of the 86 receivers who qualified in consecutive seasons, about 44 percent of them saw an increase in fantasy scoring the following year, a little less than half. RFPPGoE was slightly better than random chance at predicting improvement or regression (51 and 63 percent respectively).

However, we saw increased predictability at the extremes of this scale, particularly for regression. Of the five receivers who qualified in consecutive years with a RFPPGoE greater than two -- meaning they scored two fantasy points per game more than they should have -- four of them dropped in fantasy scoring the following year (80 percent). In fact, if the qualification of 100 targets for the second year was dropped, of the 12 total receivers to record a RFPPGoE greater than two, 10 dropped in scoring the following year (83 percent).

What I did find interesting was that the other extreme didn't have meaningful results. We'd have to go out past -3.5 RFPPGoE to improve on random chance, and that's because there's only one data point to use (Antonio Brown in 2011).

Still, it does seem that RFPPGoE can be helpful in identifying receivers who will regress going forward. Here are your five candidates for 2016.

Antonio Brown, Steelers -- RFPPGoE: 2.52

Brown has been the top PPR receiver for the past two seasons, but this analysis says that he could fall off a little in 2016. Oddly enough, this also coincides with the loss of Martavis Bryant for the season.

Over the last two years, Brown has scored 3.55 PPR points more with Bryant on the field versus off. It isn't likely that Brown goes completely in the tank in 2016, but it may be a tiebreaker between he and Odell Beckham.

Larry Fitzgerald, Cardinals -- RFPPGoE: 2.28

Larry Fitzgerald turned back the clock in 2015 with his best points per game output since 2009, but it appears that there may have been some luck involved in his resurrection. After the display Michael Floyd put on over the last eight games of the season (15.45 PPR points per game) as well as the continued growth of speedsters John Brown and J.J. Nelson, a 2016 dropoff for Fitzgerald should surprise nobody.

Brandon Marshall, Jets -- RFPPGoE: 2.22

Brandon Marshall was another receiver with a huge bounce-back 2015 campaign, as he finished in the top five in PPR scoring for wide receivers. This model suggests that may not happen again. If the likes of Devin Smith and Jace Amaro can have an impact in 2016, and/or the Jets are unable to bring back Ryan Fitzpatrick and are forced to start Geno Smith at quarterback, Marshall could see some fall in his production.

Jarvis Landry, Dolphins -- RFPPGoE: 2.18

Jarvis Landry showing up on this list shouldn't come as much of a surprise. With an average depth of target (aDOT) of just 7.4 according to Pro Football Focus (115th among qualifying receivers), Landry's receptions aren't going to create as much NEP as most others' will. If Landry sees any dip at all from the massive 167 target total he saw in 2015 (DeVante Parker, anyone?) he's bound to regress in terms of fantasy scoring.

Jeremy Maclin, Chiefs -- RFPPGoE: 2.16

Jeremy Maclin was WR19 last season in PPR points per game, but his Reception NEP was closer to the Raider duo of Michael Crabtree (WR26) and Amari Cooper (WR28). The fact that he scored more touchdowns than Travis Kelce was rather unexpected in 2015, and some decline there could certainly hurt Maclin's bottom line.